A hybrid approach for stock trend prediction based on tweets embedding and historical prices

Recently, the development of data mining and natural language processing techniques enable the relationship probe between social media and stock market volatility. The integration of natural language processing, deep learning and the financial field is irresistible. This paper proposes a hybrid approach for stock market prediction based on tweets embedding and historical prices. Different from the traditional text embedding methods, our approach takes the internal semantic features and external structural characteristics of Twitter data into account, such that the generated tweet vectors can contain more effective information. Specifically, we develop a Tweet Node algorithm for describing potential connection in Twitter data through constructing the tweet node network. Further, our model supplements emotional attributes to the Twitter representations, which are input into a deep learning model based on attention mechanism together with historical stock price. In addition, we designed a visual interactive stock prediction tool to display the result of the prediction.